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I am a PhD student at the Insitute of Cancer Research and Imperial College London researching Deep Learning in Cancer Biology under the supervision of Chris Bakal and Chris Dunsby. My PhD is part of the CRUK Accelerator award. I have an undergraduate degree in Statistics from the University of Cape Town and a master's degree in Artificial Intelligence from the University of Southampton.

My current work focuses on developing computer vision techniques to understand live 3D cancer biology. My research interests are in Geometric Deep Learning, 3D Computer Vision, Shape Analysis and Explainable AI.

Please see my organisations - Sentinal4D and MagniViT - for my work on geometric deep learning in cancer biology, and vision transformers for classification of soft tissue sarcoma from whole slide images.

Aside from my work and research, I am an avid runner, swimmer, cyclist, and footballer.

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Matt De Vries's Projects

timesformer icon timesformer

The official pytorch implementation of our paper "Is Space-Time Attention All You Need for Video Understanding?"

torch_dcec icon torch_dcec

Pytorch Deep Clustering with Convolutional Autoencoders implementation

transmil icon transmil

TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification

unet icon unet

unet for image segmentation

unetplusplus icon unetplusplus

Official Keras Implementation for UNet++ in IEEE Transactions on Medical Imaging and DLMIA 2018

vfiformer icon vfiformer

Video Frame Interpolation with Transformer (CVPR2022)

vgn icon vgn

Deep Vessel Segmentation by Learning Graphical Connectivity

vit-pytorch icon vit-pytorch

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

zerocostdl4mic icon zerocostdl4mic

ZeroCostDL4Mic: A Google Colab based no-cost toolbox to explore Deep-Learning in Microscopy

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